Identifying differentiating characteristics of internet applications using Principal Component Analysis

An accurate mapping between traffic and their corresponding applications can be very important for a wide range of network management and measurement tasks including traffic engineering, service differentiation, performance/failure monitoring, and security. Having the ability to identify the differentiating characteristics of IP applications is a crucial issue that can form the basis for the development of an efficient application identification tool. This paper presents an approach, based on principal component analysis, that is able to identify differentiating characteristics of different Internet applications, including several peer-to-peer file sharing protocols. These applications have gained tremendous popularity in the past few years and, today, traffic generated by P2P systems accounts for a major fraction of the Internet traffic and is bound to increase.

[1]  I. Jolliffe Principal Component Analysis , 2002 .

[2]  Panayiotis Mavrommatis,et al.  Identifying Known and Unknown Peer-to-Peer Traffic , 2006, Fifth IEEE International Symposium on Network Computing and Applications (NCA'06).

[3]  Andrew W. Moore,et al.  Internet traffic classification using bayesian analysis techniques , 2005, SIGMETRICS '05.

[4]  Matthew Roughan,et al.  Class-of-service mapping for QoS: a statistical signature-based approach to IP traffic classification , 2004, IMC '04.

[5]  Renata Teixeira,et al.  Traffic classification on the fly , 2006, CCRV.

[6]  Michalis Faloutsos,et al.  Transport layer identification of P2P traffic , 2004, IMC '04.

[7]  Michalis Faloutsos,et al.  Is P2P dying or just hiding? [P2P traffic measurement] , 2004, IEEE Global Telecommunications Conference, 2004. GLOBECOM '04..

[8]  Marco Mellia,et al.  TStat: TCP STatistic and Analysis Tool , 2003, QoS-IP.

[9]  Krishna P. Gummadi,et al.  Measurement, modeling, and analysis of a peer-to-peer file-sharing workload , 2003, SOSP '03.

[10]  Hui Liu,et al.  A Peer-To-Peer Traffic Identification Method Using Machine Learning , 2007, 2007 International Conference on Networking, Architecture, and Storage (NAS 2007).